Four paragraphs of preamble before the one sentence you needed.
What is actually happening
Length reads as helpfulness in training data, so padding is rewarded unless something in the request penalises it explicitly.
Everything below comes from published research, not from opinion. Each fix names the technique, states the effect its authors measured where one was reported, and links to the paper so you can check it. These were selected out of 6,235 techniques indexed from the literature.
4 fixes that hold up
01
U-curve of constraint compliance
Separating Constraint Compliance from Semantic Accuracy: A Novel Benchmark for Evaluating Instruction-Following Under Compression, arXiv:2512.17920 (2025)
A method for optimizing prompt length to ensure precise adherence to constraints. Medium-length prompts (~27 words) show the worst results due to RLHF signal conflict. Extremely short or complete prompts (~135 words with examples) ensure constraint compliance at 8.1/10 level due to signal unambiguity for the model.
97% of experiments showed a U-shaped curve.checked against the paper
Use it whenUse this technique when strict compliance with format constraints is critical: word limits, character limits, or response structure. Particularly relevant for automated systems, content generation with rigid requirements, creating product card descriptions, social media posts with length restrictions, API integrations where response format must be predictable. Apply when developing prompts for production systems where non-compliance with limits breaks application logic or user experience.
Prompt
You are an AI assistant tasked with generating concise and effective outputs that strictly adhere to specified constraints.
### Core Problem: The U-Curve of Constraint Compliance
Research indicates that AI models struggle with prompts of medium length (around 27 words) when given specific constraints. They tend to provide more verbose answers than requested, overriding the constraint signal. This is because the model detects enough context to engage its "helpful, detailed response" reflex, but not enough to prioritize the explicit constraint.
### Optimal Prompting Strategy: Extremes
To ensure strict constraint compliance:
1. **Extremely Short Prompts:** Use very few words, focusing solely on the core task and constraint. Example: "Explain photosynthesis in 35 words."
2. **Full Prompts:** Use significantly more words, often including detailed context, examples, and explicit instructions that reinforce the constraint. Example: A prompt of ~135 words with clear examples of the desired output length.
### Your Task
You need to generate a reusable prompt template that addresses the user's recurring problem: receiving four paragraphs of preamble before the single sentence they actually needed from ChatGPT. This prompt should leverage the "U-Curve of Constraint Compliance" principle to ensure conciseness.
### Prompt Generation Instructions
1. **Choose an Extreme:** Decide whether to structure the prompt as an extremely short, direct command or a more detailed, fully-formed prompt that reinforces the constraint. Given the user's problem (too much preamble), a shorter, more direct approach is likely more effective for this specific use case.
2. **Define the Constraint:** The core constraint is to avoid preamble and deliver only the essential information. This translates to a word count limit.
3. **Specify Output:** Clearly state what the output should be and its format.
4. **Reinforce the Constraint:** Explicitly mention the word limit and the need to avoid preamble.
### Reusable Prompt Template
**Option 1: Extremely Short Prompt**
Brevity is the soul of sustainability: Characterizing LLM response lengths, arXiv:2506.08686 (2025)
A method for managing LLM verbosity through direct directives in the prompt. Simple commands like 'Answer briefly' or 'Give only the minimal answer' reduce response length by 25-60%, while simultaneously improving factual accuracy and relevance by eliminating filler, repetitions, and hallucinations.
Direct and simple commands in the prompt allow reducing response length by 25-60%, not only without loss but with improvement in its factual accuracy and relevance.checked against the paper
Use it whenApply this technique when LLM generates excessively long answers with filler, repetitions, and irrelevant information. Particularly effective for obtaining specific facts, brief explanations, or when speed of processing and token economy are important. Use in chatbots, support systems, when processing large volumes of requests, in educational platforms, and everywhere where accuracy matters more than text volume. The method is suitable for any tasks where quality is more important than quantity of text.
Prompt
Ты — эксперт по работе с LLM, специализирующийся на оптимизации ответов.
**КОНТЕКСТ:**
Пользователь часто сталкивается с проблемой: LLM генерируют слишком длинные ответы, включая много лишней информации ("воды") до основного сути. Требуется создать универсальный промпт, который можно использовать для получения более кратких и точных ответов.
**ЗАДАЧА:**
Создай шаблон промпта, который пользователь сможет использовать для любой задачи, чтобы гарантированно получать более сжатые ответы от LLM. Промпт должен включать явные инструкции по минимизации "воды" и фокусировке на сути.
**[ИНСТРУКЦИЯ ПО ФОРМАТУ ОТВЕТА]**
- **Стиль:** Максимально краткий и по существу.
- **Структура:** Соответствовать поставленной задаче, без лишних вступлений и заключений.
- **Ключевое требование (метод из исследования "Brevity is the soul of sustainability"):**
- Используй директиву **`MINANS`**: "Предоставь только минимальный ответ".
- Если применимо, добавь **`ADDINFO`**: "добавь только самую необходимую дополнительную информацию".
- **Исключи**: `CONVERS` (вежливые обороты, приветствия, прощания), `REDINFO` (повторение уже сказанного), `IRREL` (нерелевантную информацию).
- Общая директива: **"Отвечай кратко"**.
**[ШАБЛОН ПРОМПТА]**
Ты — [УКАЖИ РОЛЬ ИЛИ СПЕЦИАЛИЗАЦИЮ МОДЕЛИ, НАПРИМЕР: "эксперт по личным финансам", "технический писатель", "историк"].
**КОНТЕКСТ:**
[ОПИШИ КОНТЕКСТ ЗАДАЧИ, НАПРИМЕР: "Я планирую поездку в Рим на 5 дней в октябре. Бюджет средний. Меня интересуют в основном исторические достопримечательности и хорошая еда."]
**ЗАДАЧА:**
[ЧЕТКО СФОРМУЛИРУЙ ОСНОВНУЮ ЗАДАЧУ, НАПРИМРИ: "Предложи мне 3 варианта не самых очевидных мест для посещения. Для каждого места кратко укажи, почему оно интересно."]
**[ИНСТРУКЦИЯ ПО ФОРМАТУ ОТВЕТА]**
- **Стиль:** Кратко и по существу.
- **Структура:** [УКАЖИ ЖЕЛАЕМУЮ СТРУКТУРУ, НАПРИМЕР: "нумерованный список", "таблица", "один абзац"].
- **Ключевое требование (метод из исследования "Brevity is the soul of sustainability"):**
- Предоставь **только минимальный ответ (`MINANS`)**.
- Если применимо, добавь **только самую необходимую дополнительную информацию (`ADDINFO`)**.
- **Исключи**: вежливые вступления, приветствия, заключительные фразы, повторения и нерелевантную информацию.
- **Общая директива:** Отвечай кратко.
The Sufficiency-Conciseness Trade-off in LLM Self-Explanation from an Information Bottleneck Perspective, arXiv:2602.14002 (2026)
A method for optimizing prompts through explicit answer length limitation. Research showed that LLMs generate 40-50% redundant information. Specifying a concrete word limit in the prompt allows you to reduce the response size by half while maintaining accuracy and getting more concentrated results with token savings.
LLMs generate explanations with 40-50% redundancy. Half can be discarded without loss of accuracy. The method allows you to save tokens and get more focused answers through explicit length constraints in the prompt. Instead of 'explain in detail' write 'explain in 30 words'—the model will discard the filler, keep the essence, _and accuracy will be preserved_.
Use it whenUse this technique when you need to get more focused and economical answers from LLMs without losing quality. Especially useful when working with a limited token budget, when the model generates excessively detailed explanations with fluff. Apply when creating prompts for content generation, explanations, instructions, or answering questions. Instead of vague formulations like 'explain in detail,' specify concrete length limits (for example, 'explain in 30 words'). This forces the model to
Prompt
# Sufficiency-Conciseness Prompting
## Objective
Fix the recurring problem of excessive preamble in LLM responses. Produce a concise, focused output by explicitly limiting response length.
## Task
Generate a reusable prompt template that instructs an LLM to provide a direct answer with minimal preamble.
## Core Principle: Sufficiency-Conciseness Trade-off
LLMs often include 40-50% redundant information. By setting a strict word count, we force the LLM to retain only the essential, accurate information, saving tokens and improving focus.
## Prompt Template Structure
**Instruction:** [Your specific task here]
**Constraint:** Respond in no more than [Number] words.
**Format:** [Specify desired output format, e.g., "numbered list", "single paragraph", "bullet points"]
## Example Application (for user understanding)
**Scenario:** User needs a 3-sentence summary of a complex topic.
**Prompt to LLM:**
Summarize the key principles of quantum entanglement.
Constraint: Respond in no more than 30 words.
Format: Single paragraph.
**Expected Output (concise):**
Quantum entanglement links particles so their states are correlated regardless of distance. Measuring one instantly influences the other, a phenomenon Einstein called "spooky action at a distance." This defies classical physics but is experimentally verified.
## Reusable Prompt Template for Users
Paste the following template and fill in the bracketed sections:
Controllable Abstraction in Summary Generation for Large…
Controllable Abstraction in Summary Generation for Large Language Models via Prompt Engineering, arXiv:2510.15436 (2025)
A technique for optimizing prompt length for text summarization. The optimal instruction length is 30-40 words (2-4 sentences). Short prompts give irrelevant results, long ones confuse the model. The golden mean provides maximum accuracy and control over the level of detail in the summary.
30-40 words in a prompt give better results than 10 or 100 — this is the 'golden mean' for summarization. The method allows obtaining accurate text summaries while controlling the level of detail and relevance. Too short a prompt gives the model too much freedom (result is irrelevant), too long
Use it whenApply this technique when you need to obtain a quality summary of any type of text: news, articles, blogs, documents. Particularly effective when control over the level of detail and relevance of the result is important. Use when working with noisy texts containing extra information. Suitable for regular summarization of large volumes of content, when stable accuracy is needed without complex settings. The method is universal for different LLMs and does not require special technical knowledge.
Prompt
You are an AI assistant specializing in summarization with controllable abstraction. Your goal is to provide concise and relevant summaries based on provided text and specific instructions.
**Task:** Summarize the following text.
**Instructions:**
1. **Focus:** Extract the core findings and practical implications related to prompt engineering for summarization.
2. **Abstraction Level:** Aim for a summary that is informative but avoids overly technical jargon. It should be easily understandable by someone familiar with LLMs but not necessarily an expert in prompt engineering research.
3. **Key Elements to Include:**
* The concept of an optimal prompt length ("golden mean") and its approximate word count.
* The impact of "noise" in the source text on summary quality.
* The difference in summarization quality based on text type (news, blog, academic).
* Practical advice for users based on these findings.
4. **Format:** Present the summary as a concise, coherent paragraph or a short bulleted list (max 5 points).
5. **Constraint:** The total prompt (including this instruction and the text to be summarized) should ideally be around 30-40 words for the summarization instruction part itself, to align with the "golden mean" principle.
**Text for Summarization:**
Controllable Abstraction in Summary Generation for Large Language Models via Prompt Engineering
The study investigates the optimal prompt length for summarization tasks. It found that prompts of approximately 30-40 words yield the best results, outperforming both very short (under 10 words) and very long (over 100 words) prompts. This "golden mean" allows for precise control over detail and relevance without overwhelming the model. Shorter prompts grant too much freedom, leading to irrelevant output, while longer prompts introduce "noise" that confuses the model. The research also highlights the negative impact of "noise" in the source text (e.g., formatting artifacts, ads, unrelated fragments) on summary quality. Furthermore, the type of text significantly affects summarization difficulty, with news articles being easiest, followed by blogs, and then academic papers being the most challenging due to complex terminology and logic. Practical implications include cleaning source texts and tailoring prompt complexity to the text type.
---
**Summary:**
Repeating the instruction louder. Capitals, "IMPORTANT", and three exclamation marks change nothing structural. The rule still sits in the same place, competing with the same context.
Politeness and threats. Both have been measured repeatedly across 2025 and 2026 and come out indistinguishable from noise.
Turning the temperature to zero. It reduces variation, not misunderstanding. If your request has two valid readings, you now get the wrong one reliably.
Get this fixed for your actual task
The four prompts above are written for the average case. Paste what you are actually
trying to do and the corpus will be matched against it directly. Free, no account,
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Prompt for your task
That number is low on purpose, and it is real. It is the raw similarity
of one retrieval pass. No specialist read the paper, no judge compared anything against
anything, and the first plausible match won. It is the honest score of a ten-second answer.
537techniques in the corpus address this exact symptom
You have seen 4 of them on this page and one more just now. Deciding which of the
remaining 537 actually fits your case is the work, and it is the work GetDecision does.
This page
4 techniques for the average case, generic prompts
What you just ran
one technique matched to your wording, nothing verified
Full run
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Why does ChatGPT stop following instructions I already gave?
Length reads as helpfulness in training data, so padding is rewarded unless something in the request penalises it explicitly.
Does lowering the temperature fix this?
It reduces variation, not misreading. If the request admits more than one valid interpretation, a colder model just picks the same wrong one more consistently.
Do these techniques work on reasoning models?
Some do and some do not. Each fix below carries the effect its authors measured and a link to the paper, so you can check what it was measured on.